Postdoctoral Research Associate in Statistics
Department of Statistics, 24-29 St Giles’, Oxford
Grade 7: £32,817 – £40,322 p.a.
Applications are invited for a full-time Postdoctoral Research Associate in Statistics to carry out research in the area of developing characterisations of network models and interactions with methods in statistical machine learning. You will provide guidance to junior members of the research group including project students and PhD students. You will manage your own academic research and administrative activities, prepare working theories and analyse qualitative and/or quantitative data, contribute ideas for new research projects, as well as develop ideas for generating research income. You will present detailed research proposals to senior researchers and collaborate in the preparation of research publications. You will act as a source of information and advice to other members of the group on methodologies or procedures and represent the research group at external meetings and seminars.
As the successful Postdoctoral Research Associate in Statistics, you will hold or be close to completion of a relevant PhD, together with relevant experience, in the area of probability or statistical machine learning. You will possess sufficient specialist knowledge in probability theory and statistical machine learning and have the ability to manage own academic research and associated activities. Previous experience of contributing to publications and presentations is essential. Ideally, you will possess specialist knowledge on Stein’s method or network analysis.
More information about the project can be found at Gesine Reinert.
Queries about this post should be addressed to: This email address is being protected from spambots. You need JavaScript enabled to view it..
This post is fixed-term for three years.
Only applications received before 12.00 midday on 02 June 2021 will be considered. Interviews will be held on 22 June 2021.
Contact Person : HR
Vacancy ID : 150783
Closing Date & Time : 02-Jun-2021 12:00
Contact Email : This email address is being protected from spambots. You need JavaScript enabled to view it.
